Application of Ant Colony Optimization for Job Shop Scheduling in the Pharmaceutical Industry
Bibliographic record
Abstract
Scheduling problems in the industrial sector are among the most studied optimization problems.Improving resource efficiency and minimizing production costs have become important concerns for industry managers.Seeking the best way to maximize profit is now a primary objective for any business.This is the context in which our study is positioned.It focuses on the resolution of job shop scheduling problems (JSSP).Considering that production challenges in industries are complex and require the consideration of multiple factors, we turn to the use of artificial intelligence tools for their resolution.Pharmaceutical manufacturing often involves a large number of resources, machines, and tasks, leading to high complexity in the JSSP.Ant colony optimization (ACO) is innovative and excels in its ability to handle this complexity by seeking optimal solutions while avoiding computational pitfalls.It can efficiently explore vast search spaces and leverage ant parallelism to reach the best solution in a short period of time, which is crucial in the pharmaceutical context where deadlines and quality constraints are paramount.Thus, in order to address the JSSP, this work suggests and puts into practice a method that involves the application of an ACO approach with the goal of minimizing the makespan.We validated our approach by comparing it with various algorithms through benchmarks taken from the published research.The suggested approach proved to be effective as the produced solutions were of high quality and showed that it could achieve results that are closer to the ideal solution for larger-scale issues than other algorithms with an average percentage relative error of just 0.67%.Furthermore, application of ACO in the context of BIOCARE's pharmaceutical laboratories' production led to an improvement of approximately 3 hours in their weekly planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".